Zhiyuan Zhao
Zhiyuan Zhao has a total of 15 co-authors. Here are the top co-authors by number of shared publications:
- Xiaoran Zheng
SPIC Integreted Smart Energy Technology Co., Ltd., Beijing 100080, China
2 shared publications - Yuanjun Guo
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
2 shared publications - Jiafeng Song
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
1 shared publication - Jian Guo
SPIC Integreted Smart Energy Technology Co., Ltd., Beijing 100080, China
1 shared publication - Jian Zhu
SPIC Integrated Smart Energy Technology Co., Ltd., Beijing 100080, China
1 shared publication - Jianling Sun
SPIC Integrated Smart Energy Technology Co., Ltd., Beijing 100080, China
1 shared publication
Latest Publications
- Open Access
A Dual-Layer Scheduling Method for Virtual Power Generation with an Integrated Regional Energy System
An Integrated Energy System (IES) integrates electricity, heat, and natural gas, optimizing energy use and management efficiency. These systems connect to a Virtual Power Plant (VPP) for demand response dispatch in the electricity market. However, the impact of VPP load on the IES is often overlooked, which can limit the IES’s effective market participation and stability. To address this issue, this study introduces a two-layer collaborative model to coordinate VPP scheduling for multiple IES units, aiming to improve collaboration efficiency. The upper level involves the VPP setting electricity prices based on load conditions, guiding IES units to adjust their market strategies. At the lower level, the model encourages integration and optimization of different energy types within the IES through enhanced energy interactions. Additionally, the application of the Shapley value method ensures fair benefit distribution among all IES members. This approach supports equitable economic outcomes for all participants in the energy market. The model employs a multi-strategy improved Dung Beetle Optimizer (FSGDBO) combined with commercial solver techniques for efficient problem-solving. Experimental results demonstrate that the model significantly enhances the VPP’s peak-shaving and valley-filling capabilities while preserving the economic interests of the IES alliances, thereby boosting overall energy management effectiveness.
Open AccessA Dual-Layer Scheduling Method for Virtual Power Generation with an Integrated Regional Energy System
An Integrated Energy System (IES) integrates electricity, heat, and natural gas, optimizing energy use and management efficiency. These systems connect to a Virtual Power Plant (VPP) for demand response dispatch in the electricity market. However, the impact of VPP load on the IES is often overlooked, which can limit the IES’s effective market participation and stability. To address this issue, this study introduces a two-layer collaborative model to coordinate VPP scheduling for multiple IES units, aiming to improve collaboration efficiency. The upper level involves the VPP setting electricity prices based on load conditions, guiding IES units to adjust their market strategies. At the lower level, the model encourages integration and optimization of different energy types within the IES through enhanced energy interactions. Additionally, the application of the Shapley value method ensures fair benefit distribution among all IES members. This approach supports equitable economic outcomes for all participants in the energy market. The model employs a multi-strategy improved Dung Beetle Optimizer (FSGDBO) combined with commercial solver techniques for efficient problem-solving. Experimental results demonstrate that the model significantly enhances the VPP’s peak-shaving and valley-filling capabilities while preserving the economic interests of the IES alliances, thereby boosting overall energy management effectiveness.
- Open Access
Time-Series Power Forecasting for Wind and Solar Energy Based on the SL-Transformer
As the urgency to adopt renewable energy sources escalates, so does the need for accurate forecasting of power output, particularly for wind and solar power. Existing models often struggle with noise and temporal intricacies, necessitating more robust solutions. In response, our study presents the SL-Transformer, a novel method rooted in the deep learning paradigm tailored for green energy power forecasting. To ensure a reliable basis for further analysis and modeling, free from noise and outliers, we employed the SG filter and LOF algorithm for data cleansing. Moreover, we incorporated a self-attention mechanism, enhancing the model’s ability to discern and dynamically fine-tune input data weights. When benchmarked against other premier deep learning models, the SL-Transformer distinctly outperforms them. Notably, it achieves a near-perfect value of 0.9989 and a significantly low SMAPE of 5.8507% in wind power predictions. For solar energy forecasting, the SL-Transformer has achieved a SMAPE of 4.2156%, signifying a commendable improvement of 15% over competing models. The experimental results demonstrate the efficacy of the SL-Transformer in wind and solar energy forecasting.Open AccessTime-Series Power Forecasting for Wind and Solar Energy Based on the SL-Transformer
As the urgency to adopt renewable energy sources escalates, so does the need for accurate forecasting of power output, particularly for wind and solar power. Existing models often struggle with noise and temporal intricacies, necessitating more robust solutions. In response, our study presents the SL-Transformer, a novel method rooted in the deep learning paradigm tailored for green energy power forecasting. To ensure a reliable basis for further analysis and modeling, free from noise and outliers, we employed the SG filter and LOF algorithm for data cleansing. Moreover, we incorporated a self-attention mechanism, enhancing the model’s ability to discern and dynamically fine-tune input data weights. When benchmarked against other premier deep learning models, the SL-Transformer distinctly outperforms them. Notably, it achieves a near-perfect value of 0.9989 and a significantly low SMAPE of 5.8507% in wind power predictions. For solar energy forecasting, the SL-Transformer has achieved a SMAPE of 4.2156%, signifying a commendable improvement of 15% over competing models. The experimental results demonstrate the efficacy of the SL-Transformer in wind and solar energy forecasting.